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Are you interested in solving large‑scale search problems and building next‑generation image and video search experiences powered by machine learning and large language models (LLMs)? The Bing Multimedia Group builds global image and video search feeds and content discovery experiences across Bing, Windows, Edge, MSN, and Azure. Our systems serve users in 100+ markets worldwide, handling billions of queries and content interactions every day. We are hiring Senior Applied Scientist to work on image and video retrieval, ranking, relevance optimization, and LLM‑powered search APIs and applications, driving real user impact in production systems at global scale.
Job Responsibility:
Design, develop, and improve large‑scale image and video search algorithms, with a focus on retrieval, recall, ranking, and relevance optimization
Build and operate multi‑stage search pipelines that power production image and video search experiences
Develop LLM‑powered search APIs and applications, integrating large language models with search, retrieval, and ranking systems to enable new developer and user experiences
Apply machine learning, deep learning, multimodal models, and LLM techniques to improve search quality, engagement, and long‑term product growth
Own the end‑to‑end machine learning lifecycle: Problem formulation and metric definition, Offline training, evaluation, and optimization, Online experimentation (A/B testing), Production deployment and monitoring
Build billion‑scale, low‑latency ML systems integrated into Bing and Windows search runtimes
Collaborate with researchers and partner teams to explore and productionize state‑of‑the‑art techniques for image, video, and LLM‑driven search scenarios
Requirements:
Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
OR equivalent experience
Proven track record of impact in search engines or recommendation systems
Solid understanding of search engine fundamentals, including query, user, and content understanding, retrieval / recall architectures, ranking and relevance optimization
Experience building production machine learning systems, from data modeling to online deployment
Hands‑on experience in one or more of the following areas: Information retrieval, Recommendation systems, Computer vision or video understanding, Deep learning, NLP, or multimodal learning
Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow)
Solid communication and collaboration skills in cross‑functional, global teams
MS or PhD in Computer Science or a related field
Experience with large‑scale image or video search systems
Familiarity with multi‑stage retrieval and ranking pipelines in production search engines
Experience applying LLMs or multimodal foundation models to search, recommendation, or API‑based applications
Experience with search APIs or developer‑facing ML platforms
Experience with big‑data systems and pipelines (e.g., Hadoop, Spark)
Understanding of search quality metrics, online experimentation, and long‑term relevance strategies
Experience building high‑performance, low‑latency systems at scale